class_docstr: This class represents the internal state of individual tracked objects observed as bbox.
  
  Args:
      bbox (np.array): bbox in [x1,y1,x2,y2,score] format.
      delta_t (int): delta_t of previous observation
class_docstr_tok: ["This","class","represents","the","internal","state","of","individual","tracked","objects","observed","as","bbox",".","Args",":","bbox","(","np.array",")",":","bbox","in","[","x1",",","y1",",","x2",",","y2",",","score","]","format",".","delta_t","(","int",")",":","delta_t","of","previous","observation"]
docstr: Advances the state vector and returns the predicted bounding box estimate.
docstr_tok: ["Advances","the","state","vector","and","returns","the","predicted","bounding","box","estimate","."]
code: def predict(self):
      """
      Advances the state vector and returns the predicted bounding box estimate.
      """
      if (self.kf.x[6] + self.kf.x[2]) <= 0:
          self.kf.x[6] *= 0.0
  
      self.kf.predict()
      self.age += 1
      if self.time_since_update > 0:
          self.hit_streak = 0
      self.time_since_update += 1
      self.history.append(
          convert_x_to_bbox(self.kf.x, score=self.score)
      )
      return self.history[-1]
  
file_path: PaddleDetection/deploy/pptracking/python/mot/tracker/ocsort_tracker.py
imports: import time
  import numpy
  from ..matching.ocsort_matching import associate, linear_assignment, iou_batch, associate_only_iou
  from ..motion.ocsort_kalman_filter import OCSORTKalmanFilter
label_desc: Use image node_id 3 for calling the KalmanBoxTracker obj's underlying member method code with example usage: obj.predict() and returns: self